Consider the following data for a dependent variable y and two independent variables, 1 and . 30 12 94 47 11 108 25 18 113 51 16 178 41 6 94 52 20 176 74 171 37 13 118 60 14 143 77 16 211 Round your all answers to two decimal places. Enter negative values as negative numbers, if necessary. a. Develop an estimated regression equation relating y to x1. Predict y if = 35. %3D b. Develop an estimated regression equation relating y to I2 Predict y if xa = 15. c. Develop an estimated regssion equation relating y to 1 and r2 Predict v if % = 35 and %a = 15.

Consider the following data for a dependent variable y and two independent variables, 1 and . 30 12 94 47 11 108 25 18 113 51 16 178 41 6 94 52 20 176 74 171 37 13 118 60 14 143 77 16 211 Round your all answers to two decimal places. Enter negative values as negative numbers, if necessary. a. Develop an estimated regression equation relating y to x1. Predict y if = 35. %3D b. Develop an estimated regression equation relating y to I2 Predict y if xa = 15. c. Develop an estimated regssion equation relating y to 1 and r2 Predict v if % = 35 and %a = 15.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

See similar textbooks

Similar questions

Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. Using the 68-95-99.7 rule, between what two values would approximately 95% of the observed responses, y, fall when x = 7?
Consider the following regression equation specied for 2-period panel data: where i = 1; 2; :::N and t = 1; 2. If you expect that β_1 is positive, but the correlation between Δx_i and Δu_i is negative, thenwhat is the bias in the OLS estimator of β_1 in the first-differenced equation?
Consider the following data for two variables, x and y. x 9 32 18 15 26 y 9 19 20 15 22 Develop an estimated regression equation for the data of the form ŷ = b0 + b1x + b2x2. (Round b0 to two decimal places and b1 to three decimal places and b2 to four decimal places.) ŷ = (c) Use the model from part (b) to predict the value of y when x = 20. (Round your answer to two decimal places.)
An investigation into the relationship between an adolescent mother's age x in years and the birth weight y of her baby in grams yielded the regression equation y= - 1163.45 + 245.15x as well as r = .88369, r2= .78091, SSE = 337212.45, and s= 205.30844 1) What is the predicted birth weight for a baby brn to a 17 year old woman? 2) What is the propotion of the variability in the weights of babies born to adolescent mothers that is accounted for by the mother's age? 3) For every additional year in the mother's age that mean birth weight of the baby? (a) increases by about 245g (b) decreases by about 245g (c) increases by about 1163g (d) increases by about 1163g (e) changes by an amount that cannot be determined from the information given.
A researcher conducted a number of descriptive statistics for two variables X and Y. They were as follows: SP = -20; SSx = 4; My = 7; Mx = 3 What is b equal to? What is a equal to? Using b and a construct a regression equation, and then using the regression equation, calculate the value of predicted Y when X = 2?
Given are five observations collected in a regression study on two variables. xi 2 6 9 13 20 yi 7 18 9 26 23 Compute b0 and b1 (to 1 decimal).b1 b0 Complete the estimated regression equation (to 1 decimal).^y = + x Use the estimated regression equation to predict the value of y when x = 6 (to 1 decimal).^y =
In a regression analysis involving 30 observations, the following estimated regression equation was obtained. ŷ = 16.7 + 3.5x1 − 2.3x2 + 7.9x3 + 2.9x4 (a)Interpret b1 in this estimated regression equation. -b1 = 7.9 is an estimate of the change in y corresponding to a 1 unit change in x3 when x1, x2, and x4 are held constant. -b1 = 2.9 is an estimate of the change in y corresponding to a 1 unit change in x4 when x1, x2, and x3 are held constant. -b1 = 3.5 is an estimate of the change in y corresponding to a 1 unit change in x1 when x2, x3, and x4 are held constant. -b1 = 3.5 is an estimate of the change in y corresponding to a 1 unit change in x2 when x1, x3, and x4 are held constant. -b1 = −2.3 is an estimate of the change in y corresponding to a 1 unit change in x1 when x2, x3, and x4 are held constant.Interpret b2 in this estimated regression equation. -b2 = −2.3 is an estimate of the change in y corresponding to a 1 unit change in x1 when x2, x3, and x4 are held…
The following X and Y scores produce SSx=2 and SP=8. What is regression equation for predicting Y? X Y 1 2 2 3 3 10
The following estimated regression model was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).ŷ = 30 + 0.7x1 + 3x2Also provided are SST = 1200 and SSE = 384.The yearly income of a 24-year-old female individual is _____.
The following estimated regression equation based on 10 observations was presented. ŷ = 29.1260 + 0.5306x1 + 0.4680x2 The values of SST and SSR are 6,728.125 and 6,215.375, respectively. (a) Find SSE. SSE = (b) Compute R2. (Round your answer to three decimal places.) R2 = (c) Compute Ra2. (Round your answer to three decimal places.) Ra2 = (d) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation.The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation.